ORIGINAL
RESEARCH
J.F.A. Jansen
H.E. Stambuk
J.A. Koutcher
A. Shukla-Dave
Non-Gaussian Analysis of Diffusion-Weighted MR
Imaging in Head and Neck Squamous Cell
Carcinoma: A Feasibility Study
BACKGROUND AND PURPOSE: Water in biological structures often displays non-Gaussian diffusion
behavior. The objective of this study was to test the feasibility of non-Gaussian fitting by using the
kurtosis model of the signal intensity decay curves obtained from DWI by using an extended range of
b-values in studies of phantoms and HNSCC.
MATERIALS AND METHODS: Seventeen patients with HNSCC underwent DWI by using 6 b-factors (0,
50 –1500 s/mm2) at 1.5T. Monoexponential (yielding ADCmono) and non-Gaussian kurtosis (yielding
apparent diffusion coefficient Dapp and apparent kurtosis coefficient Kapp) fits were performed on a
voxel-by-voxel basis in selected regions of interest (primary tumors, metastatic lymph nodes, and
spinal cord). DWI studies were also performed on phantoms containing either water or homogenized
asparagus. To determine whether the kurtosis model provided a significantly better fit than did the
monoexponential model, an F test was performed. Spearman correlation coefficients were calculated
to assess correlations between Kapp and Dapp.
RESULTS: The kurtosis model fit the experimental data points significantly better than did the monoexponential model (P ⬍ .05). Dapp was approximately twice the value of ADCmono (eg, in neck nodal
metastases Dapp was 1.54 and ADCmono was 0.84). Kapp showed a weak Spearman correlation with
Dapp in a homogenized asparagus phantom and for 44% of tumor lesions.
CONCLUSIONS: The use of kurtosis modeling to fit DWI data acquired by using an extended b-value
range in HNSCC is feasible and yields a significantly better fit of the data than does monoexponential
modeling. It also provides an additional parameter, Kapp, potentially with added value.
ABBREVIATIONS: ADC ⫽ apparent diffusion coefficient; BOT ⫽ base of tongue; DWI ⫽ diffusion-
he MR imaging technique known as DWI allows measurement of water diffusivity.1 Because freedom of translational motion of water molecules is hindered by interactions
with other molecules and cell membranes, DWI abnormalities
can reflect changes of tissue organization at the cellular level.2,3
These microstructural changes affect the motion of water molecules, and consequently alter the water diffusion properties
and thus the MR imaging signal intensity. The signal intensity
loss in DWI can be quantified by using the ADC, which is a
measure of the average molecular motion that is affected by
cellular organization and integrity. In the simplest models, the
distribution of a water molecule diffusing from one location to
another in a certain period of time is considered to have a
Gaussian form with its width proportional to the ADC.2 HowReceived June 10, 2009; accepted after revision September 7.
From the Departments of Medical Physics (J.F.A.J., J.A.K., A.S.-D.), Radiology (J.F.A.J.,
H.E.S., J.A.K., A.S.-D.), and Medicine (J.A.K.), Memorial Sloan-Kettering Cancer Center,
New York, New York.
This work was supported by National Cancer Institute/National Institutes of Health (Grant
1 R01 CA115895).
Previously presented in part as abstract 1319 at: 17th Annual Meeting of the International
Society of Magnetic Resonance in Medicine, April, 18 –24, 2009, Honolulu, Hawaii.
Please address correspondence to Amita Shukla-Dave, PhD, Department of Medical Physics
and Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY
10021; e-mail: [email protected]
Indicates open access to non-subscribers at www.ajnr.org
DOI 10.3174/ajnr.A1919
ever, water in biological structures often displays non-Gaussian diffusion behavior.4 As a result, the MR signal intensity
decay in tissue is not a simple monoexponential function of
the b-value.3,5
Several approaches have been used to model the nonlinear
decay of DWI signal intensity when more than 2 b-values are
acquired. These approaches include biexponential fitting,
from which 2 components that hypothetically reflect 2 separate biophysical compartments can be derived,6 stretchedexponential fitting, which describes diffusion-related signal
intensity decay as a continuous distribution of sources decaying at different rates,7 and diffusional kurtosis analysis, which
takes into account non-Gaussian properties of water diffusion
by measuring the kurtosis.8 Kurtosis represents the extent to
which the diffusion pattern of the water molecules deviates
from a perfect Gaussian curve. Unlike the biexponential
model, the stretched-exponential and the kurtosis methods do
not make assumptions regarding the number of biophysical
compartments or even the existence of multiple compartments.3 From the kurtosis analysis, 2 parameters are derived:
the apparent diffusion coefficient (Dapp) and the apparent
kurtosis coefficient (Kapp).
In the HN region, DWI has been used for characterizing
and differentiating benign and malignant pathology in patients,9-19 evaluating treatment-induced tissue changes, especially after chemoradiation therapy,20-22 and assessing persistent or recurrent cancer.23,24 In previous DWI studies in HN
AJNR Am J Neuroradiol 31:741– 48 兩 Apr 2010 兩 www.ajnr.org
741
ORIGINAL RESEARCH
T
HEAD & NECK
weighted imaging; Gd-DTPA ⫽ gadolinium-diethylene-triamine pentaacetic acid; HN ⫽ head and
neck; HNSCC ⫽ head and neck squamous cell carcinoma; NPC ⫽ nasopharyngeal cancer; ROI ⫽
region of interest; SCC ⫽ squamous cell carcinoma; SNR ⫽ signal intensity-to-noise ratio
cancer patients, a combination of 2 or 3 b-factors (eg, 0, 500,
and 1000 s/mm2) was usually acquired, and the ADC values
(ADCmono) were calculated assuming that the signal intensity
decay is a monoexponential function.12,18,19,25 To our knowledge, no study has considered the non-Gaussian diffusion behavior of DWI data in HNSCC. The kurtosis model may lead
to better fit of the experimental data.8 The diffusion parameters Dapp and Kapp provided by this model could offer potential
value for longitudinal studies assessing early response in patients with HNSCC undergoing chemoradiation therapy.
The objective of this study was to test the feasibility of nonGaussian fitting by using the kurtosis model of the signal intensity decay curves obtained from DWI by using an extended
range of b-values in studies of phantoms and HNSCC.
neurovascular phased array coil for signal intensity reception and a
body coil for transmission. Accuracy was assessed on both the settings
(1.5T by using a 4-channel neurovascular array coil, 1.5T by using an
8-channel neurovascular array coil) with phantoms at 25°C.
The patient study was done on either 4-channel (n ⫽ 10) or
8-channel (n ⫽ 7) coils and consisted of MR imaging covering the
entire neck or oral cavity/tongue or nasopharynx. The standard MR
acquisition parameters were as follows: rapid scout images, multiplanar (axial, coronal, and sagittal) T2-weighted, fat-suppressed, fast
spin-echo images (TR, 5000 ms; TE, 102 ms; averages, 2; matrix,
256 ⫻ 256; section thickness, 5.0 mm; and gap, 2.5 mm for neck
survey; for oral cavity/tongue or nasopharynx imaging, section thickness, 4.0 mm; gap, 1.0 mm), multiplanar T1-weighted images (TR,
675 ms; TE, 8 ms; averages, 2; section thickness, 5.0 mm; gap, 2.5 mm;
matrix, 256 ⫻ 256). Standard T1- and T2-weighted imaging was followed by DWI. After DWI, Gd-DTPA contrast agent was injected and
postcontrast multiplanar T1-weighted images were acquired.
For patients, diffusion-weighted images were obtained by using
single-shot spin-echo echo-planar imaging with a pair of rectangular
gradient pulses along 3 orthogonal axes. The imaging parameters
were as follows: TR, 4000 ms; TE, 85–98 ms; averages, 4; section thickness, 5.0 mm; gap, 0 mm; FOV, 20 –26 cm; matrix, 128 ⫻ 128; and 3
diffusion sensitizing directions. The b-values were 0, 50, 100, 500, 750,
1000, and 1500 s/mm2. The duration of the DWI component of the
examination was approximately 8 minutes. Four to 10 sections were
acquired to cover the lesion.
DWI experiments were performed on phantoms by using the
same type of sequence and b-values as for the HNSCC patients. There
is a difference in loading of the coil between the studies of phantoms
and patients. To obtain the best SNR characteristics in the phantom
study, the experiments were performed with similar MR parameters
as detailed above except for averages of 16 and section thickness of
15.0 mm.
Materials and Methods
Image Analysis
Table 1: Patient characteristics
Subject
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Age (y)/Sex
65/M
75/F
84/M
55/M
70/M
78/M
27/F
60/M
63/M
66/M
51/M
60/M
51/F
51/M
68/M
55/F
49/F
Primary
Cancer
NPC
Tonsil
BOT
BOT
Tonsil
BOT
Tonsil
Tonsil
BOT
BOT
BOT
BOT
Tonsil
Tonsil
Tonsil
Tonsil
NPC
ROIs
Primary, 2 nodes, and spinal cord
Primary, node, and spinal cord
Primary and spinal cord
Primary, 2 nodes, and spinal cord
Primary, node, and spinal cord
Primary and node
Primary, 2 nodes, and spinal cord
Primary and node
Primary, node, and spinal cord
3 nodes and spinal cord
Primary and node
Primary and 3 nodes
Primary, 2 nodes, and spinal cord
Primary, 2 nodes, and spinal cord
3 nodes and spinal cord
Primary and node
Primary, 2 nodes, and spinal cord
Phantoms
Phantoms were prepared containing either Gd-DTPA-doped water
or homogenized asparagus.8 Gd-DTPA was obtained from Magnevist
(Bayer HealthCare Pharmaceuticals, Wayne, New Jersey).
Patients
Our institutional review board approved and issued a waiver of informed consent for our retrospective study, which was compliant
with the Health Insurance Portability and Accountability Act. Between June 2008 and June 2009, 17 consecutive patients with HNSCC,
referred for MR imaging by physicians at our institution, underwent a
pretreatment clinical MR imaging examination that included DWI.
Patient characteristics are given in Table 1.
SCC pathology was confirmed by using needle biopsy at the primary HN tumor location at least 1 week before the MR imaging examination. Of the sites that contributed to the mean DWI parameter
values for these 17 patients, primary tumors included 6 base of
tongue, 7 tonsil, and 2 nasopharyngeal tumors, 28 were metastatic
neck lymph nodes, and 12 were spinal cord (which served as a control
region).
MR Imaging Data Acquisition
MR imaging data were acquired on a 1.5T Excite scanner (GE Healthcare, Milwaukee, Wisconsin) by using either a 4-channel or 8-channel
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Image processing was performed by using customized software in
Matlab (MathWorks, Natick, Massachusetts), based on SPM5 software routines (Wellcome Department of Cognitive Neurology, London, United Kingdom). The individual diffusion-weighted images
for the 3 directions were automatically averaged on the scanner;
hence, for subsequent analyses only the averaged diffusion-weighted
images were included. For all of the 6 DWI images with a b-value ⬎0
s/mm2, a corresponding image with b ⫽ 0 s/mm2 was obtained. All 6
b ⫽ 0 images were aligned to correct for motion, by using an affine
transformation in SPM5. Subsequently, all corresponding DWI images with a b-value ⬎0 s/mm2 were transformed by using the same
parameters obtained from the b ⫽ 0 images realignment. An averaged
b ⫽ 0 image was obtained after realignment, with optimal SNR characteristics. Next, Gaussian smoothing was applied on all images with
a full width at half maximum of 3 mm, which is approximately 3 times
the in-plane voxel size.
For phantom data analysis, a ROI created by using the MRIcro
software26 (www.sph.sc.edu/comd/rorden/mricro.html) was positioned within the phantom of size approximately 64 mm3. Statistics
(mean and SD) were derived from the values within the ROI. Combining data from both 4- and 8-channel coils was proved to be feasible, and no differences in ADC values were noted.
For each patient, masks were created by using MRIcro to select a
ROI for each lesion (primary tumors and nodes). ROIs were manually
drawn by an experienced neuroradiologist (with ⬎10 years of expe-
rience in reading MR imaging of the head and neck) on the averaged
b ⫽ 0 image. All of the sections containing the tumor or nodal metastasis were outlined and analyzed. Fits were performed for all ROIs on
a voxel-by-voxel basis with a Marquardt-Levenberg algorithm implemented in Matlab, as described previously by Lu et al.3 SNR for all
ROIs for all diffusion-weighted images were calculated by dividing
the maximum signal intensity within the ROI by the SD of the noise
within an ROI of identical size outside the body.
The signal intensity versus b-factor from each voxel in the ROI, as
extracted from DWI datasets acquired in the presence of diffusionsensitizing gradients, was fitted with both monoexponential functions and non-monoexponential decay functions of the form
ln[S(b)] ⫽ ln[S(0)] ⫺ b ⫻ Dapp ⫹ 1/6b2 ⫻ Dapp2 ⫻ Kapp,
where S is the signal intensity (arbitrary units), b is the b-value (s/
mm2), Dapp is the apparent diffusion coefficient (10⫺3 mm2/s), and
Kapp, a dimensionless parameter, is the apparent kurtosis coefficient.
The fit parameters were not assigned initial values for the curve fitting. In addition to non-Gaussian fitting, monoexponential fitting
was performed by using Kapp ⫽ 0 in the equation, yielding ADCmono.
Heterogeneity of tumors was assessed by computing the SD of a
normalized distribution of the voxels within each ROI.27 Toward this
end, the DWI-derived parameters ADCmono, Dapp, and Kapp were first
normalized for each individual ROI to a scale of [0, 100], with 0 as the
minimum and 100 as the maximum value. Subsequently, the SD was
calculated from these normalized distributions. This procedure
yielded heterogeneity measures hADCmono, hDapp, and hKapp.
Additionally, necrosis was assessed by the radiologist separately
for the primary tumor and the neck nodal metastasis in each patient
on both the pre-T2-weighted images and the post Gd-DTPA contrast
T1-weighted images by means of visual inspection. Necrosis is hyperintense on T2-weighted images and hypointense on postcontrast T1weighted images. The MR imaging reading was scored on a scale of [0,
1], with 0 indicating no necrosis and 1 indicating necrosis (which
included mild to severe necrosis).
Statistical Analysis
Statistical tests were performed in SPSS for Windows (release 15.0.1;
SPSS, Chicago, Illinois).
To test the goodness of fit for the monoexponential and kurtosis
models, the reduced ␹2 was calculated on a voxel-by-voxel basis,
which is the sum of the differences between observed and expected
outcome values, each squared and divided by the expectation, divided
by the number of degrees of freedom in the model. To determine
whether the kurtosis model provided a statistically improved fit over
the monoexponential model, the differences between the results of
the 2 approaches were tested by using an F test of the resulting sum of
squares values, with P ⬍ .05 denoting statistical significance. In detail,
the monoexponential fitting model has 5 degrees of freedom (7 data
points minus 2 variables), and the kurtosis model has 4 degrees of
freedom. In this situation, the ratio of the relative increase of sum of
squares divided by the increase of degrees of freedom has to be
⬎F(0.05, 1, 4) ⫽ 4.5.
To assess whether the kurtosis analysis also yields potential additional information to the standard ADC value, in terms of Kapp, the
Spearman rank-order correlation coefficient for Kapp and Dapp was
calculated for both phantom and patient data. A high absolute Spearman correlation coefficient (ⱍ␳ⱍ) between Kapp and Dapp would indicate that Kapp does not have additional information, whereas a weak
Fig 1. Logarithm of the MR signal intensity decay averaged over all voxels within the right
node of patient 12 as a function of the b-value. The black crosshairs represent the
experimental data, and the solid lines are the fitted curves (red is the monoexponential fit,
blue is the non-Gaussian kurtosis fit).
correlation coefficient might indicate that Kapp is independent of Dapp
and therefore does reflect additional information.8
To explore the potentially increased sensitivity of Dapp to reflect
heterogeneity over ADCmono, the differences in heterogeneity measures hADCmono and hDapp were statistically tested by using a 2-sided
Student t test. Additionally, to investigate whether the diffusion parameters, the potential additional information of the kurtosis analysis, and the heterogeneity values of ADCmono, Dapp, and Kapp, are
affected by necrosis, tumor lesions were divided into 2 categories: 0 ⫽
no necrosis, 1 ⫽ necrosis. Subsequently, a 2-sided Student t test was
applied on these measures to assess statistical differences between
necrotic and non-necrotic lesions.
Results
Phantom Experiments
Phantom experiments revealed perfect Gaussian behavior for
the phantom containing Gd-DTPA-doped water (ADCmono ⫽
2.29 ⫾ 0.01 10⫺3 mm2/s, Dapp ⫽ 2.31 ⫾ 0.02 10⫺3 mm2/s, and
Kapp ⫽ 0.02 ⫾ 0.02) and non-Gaussian behavior for the phantom containing homogenized asparagus (ADCmono ⫽ 1.55 ⫾
0.07 10⫺3 mm2/s, Dapp ⫽ 1.75 ⫾ 0.08 10⫺3 mm2/s, and Kapp ⫽
0.28 ⫾ 0.05). The Dapp values from the non-Gaussian kurtosis
model were higher than the ADCmono values from the monoexponential fit, as reported previously.3 The Kapp in the homogenized asparagus phantom was substantially higher than
the Kapp in the water phantom, reflecting a significant departure from Gaussian diffusion.8
Comparison of Fitting Methods
Figure 1 provides an example of the signal intensity decay
curve of mean intensity of the right node of patient 12, as a
function of the b-value. The plot is semilogarithmic, and the
data points display a nonlinear decay. Also presented in this
plot are the fitting curves from the monoexponential fitting
procedure and the diffusional kurtosis analysis. It can be
clearly seen that the non-Gaussian kurtosis analysis fits the
data points substantially better than does the monoexponenAJNR Am J Neuroradiol 31:741– 48 兩 Apr 2010 兩 www.ajnr.org
743
Table 2: Average estimated diffusion coefficients derived from
monoexponential fitting and diffusional kurtosis analysis in patients
with squamous cell carcinoma head and neck cancer (n ⴝ 17)
Region (n)
Node (28)
BOT primary (6)
Tonsil primary (7)
NPC primary (2)
Spine (12)
ADCmono
0.84 ⫾ 0.24
0.67 ⫾ 0.22
0.78 ⫾ 0.20
1.16 ⫾ 0.19
0.74 ⫾ 0.08
Dapp
1.54 ⫾ 0.45
1.32 ⫾ 0.35
1.46 ⫾ 0.41
2.21 ⫾ 0.05
1.44 ⫾ 0.29
Kapp
1.38 ⫾ 0.37
1.69 ⫾ 0.54
2.91 ⫾ 3.92
0.92 ⫾ 0.17
1.41 ⫾ 0.15
␳ (Dapp and
Kapp)
⫺0.49 ⫾ 0.35
⫺0.52 ⫾ 0.42
⫺0.54 ⫾ 0.27
⫺0.21 ⫾ 0.34
⫺0.26 ⫾ 0.42
Note:—n indicates number of regions (over all squamous cell carcinoma patients) contributing to the average; ADCmono, apparent diffusion coefficient obtained from monoexponential fitting (10⫺3 mm2/s); Dapp, apparent diffusion coefficient obtained from diffusional kurtosis analysis (10⫺3 mm2/s); Kapp, apparent kurtosis coefficient obtained from
diffusional kurtosis analysis (dimensionless); ␳ (Dapp and Kapp), Spearman rank-order
correlation coefficient for correlation between Kapp and Dapp. Averages are presented as
mean ⫾ SD
tial fitting procedure. To assess whether these beneficial fit
characteristics were also significant, an F test was performed
on all ROIs. The F ratio obtained in this case (Fig 1) was 26.8,
hence greater than F(0.05, 1, 4) ⫽ 4.5, indicating that the nonGaussian kurtosis analysis was significantly (P ⬍ .05) better
than the monoexponential fit. For all individual cases in the
whole population, the non-Gaussian kurtosis analysis was always significantly better than the monoexponential fit, as calculated from the F test (P ⬍ .05). The median reduced ␹2 of all
ROIs from the entire population was 0.008 (range,
0.001– 0.15) for the monoexponential fit and 0.002 (range,
0.0003– 0.15) for the kurtosis fit. The median SNR within the
ROIs in the diffusion-weighted images acquired with the highest b-value (b ⫽ 1500 s/mm2) was 42 (range, 21– 87).
Patients
In total, 17 patients were successfully scanned by using the
extended b-value protocol. Table 2 shows the average estimated diffusion coefficients for various tissue structures, obtained by using monoexponential fitting and diffusional kurtosis analysis.
The ADC obtained from the diffusional kurtosis analysis
(Dapp) was about twice the value of the ADC obtained by
monoexponential fitting (ADCmono) (Table 2). Figure 2 shows
the MR image series obtained from the oral cavity of patient
12. In Fig 2C–F, the calculated fitting parameters for the right
node are visualized by using a mask overlay on the realigned
mean b ⫽ 0 image. From the reduced ␹2 error estimate in Fig
2F, it can be observed that the fit deteriorates near the rim of
the node. In Fig 2G–I, histogram distribution plots based on
monoexponential and non-Gaussian fitting are shown. In Fig
2I, the histogram for Kapp displays the presence of 2 distinct
distribution peaks, at 0.5 and 1.1.
Potential Additional Information
Aside from providing a better fit, the non-Gaussian kurtosis
analysis may also yield new information (ie, the Kapp [apparent
kurtosis coefficient]) in addition to the standard ADC value.
To assess the potential added value of this parameter, for all
voxels of the tumor highlighted in Fig 3A (right node of patient
12), the Dapp was plotted as a function of the Kapp. The resulting scatterplot is given in Fig 3C. To investigate this relationship in a quantitative way, a Spearman rank-order correlation
coefficient was calculated. The Spearman rank-order correla744
Jansen 兩 AJNR 31 兩 Apr 2010 兩 www.ajnr.org
tion coefficient for this case was ⫺0.34, indicative of a weak
correlation between the Kapp and Dapp. Fig 3D displays the
Dapp versus Kapp plot of the primary tumor of patient 3 (Fig
3B). In this case, the Spearman rank-order correlation is
⫺0.90. The range of absolute Spearman rho (ⱍ␳ⱍ) for all tumor
lesions (n ⫽ 43) of the population was 0.1 to 0.95, though for
44% of the lesions (n ⫽ 19) ⱍ␳ⱍ ⬍ 0.5.
The heterogeneity values for the DWI-derived parameters,
averaged over all tumor lesions (n ⫽ 43), were hADCmono ⫽
18.2 ⫾ 3.6, hDapp ⫽ 19.2 ⫾ 3.5, and hKapp ⫽ 12.5 ⫾ 4.6
(mean ⫾ SD). The heterogeneity measure for the diffusion
coefficient obtained by using the kurtosis analysis hDapp was
statistically higher (P ⬍ .05) than the value hADCmono obtained by using the monoexponential analysis.
We identified 25 tumor lesions with necrosis and 18 lesions
with no necrosis; specifically, 23 metastatic neck nodes and 2
primary tumors were necrotic, whereas 5 nodes and 14 primary tumors were not necrotic. Due to the limited number of
necrotic primary tumors (n ⫽ 2), no separate analysis was
performed for the primary lesions. However, a separate analysis for the metastatic lymph nodes was performed, and it
yielded significantly higher ADCmono and Dapp values and significantly lower Kapp values for the necrotic nodes (P ⬍ .01).
The differences in Spearman correlation coefficient between
Kapp and Dapp and heterogeneity values hADCmono, hDapp, and
hKapp were not significant for the necrotic (n ⫽ 23) versus the
non-necrotic (n ⫽ 5) neck nodal metastases (P ⬎ .1).
Discussion
In this study, we demonstrated the feasibility of using diffusional non-Gaussian (kurtosis) modeling to fit DWI data acquired by using an extended b-value range in studies of phantoms and HN tumors. Diffusion kurtosis represents the extent
to which the diffusion pattern of the water molecules deviates
from a perfect Gaussian curve.8 Because the deviation from
Gaussian behavior is governed by the complexity of the tissue
in which the water is diffusing, the diffusional kurtosis can be
regarded as an index of tissue microstructural complexity.8
The degree of diffusional non-Gaussianity may be clinically
relevant, as it is thought to arise from diffusion barriers, such
as cell membranes and organelles, and water compartments.8
There is substantial evidence that the diffusion decay is of
non-monoexponential origin; however, the exact nature is not
well understood.6 Studies have been done on brain and prostate cancer by using non-monoexponential analysis with either biexponential or stretched exponential or kurtosis analysis.5,6,8,28-30 In the present study we have used the approach of
the diffusional kurtosis model, which does not provide information regarding intra- and extracellular compartments but
allows an additional parameter, Kapp, to be obtained that may
potentially be more sensitive to pathologic changes.8 Other
explanations for non-monoexponential diffusion behavior include macromolecule binding of water,31 restricted diffusion,32 and a regime of fast exchange between multiple compartments.33 The elucidation of the exact nature of the nonmonoexponential diffusion behavior and the theoretic
validity of the kurtosis model were beyond the scope of the
present study. A fair comparison of the kurtosis analysis with
any other non-Gaussian analyses was beyond the scope of this
study. A study by Minati et al compared 2 non-Gaussian mod-
Fig 2. Axial MR images from the oral cavity of a male patient diagnosed with base of tongue cancer (patient 12). A, T2 short tau inversion recovery image. B, Realigned mean b ⫽ 0
image. The red and white arrows in A indicate the primary tumor and metastatic nodes, respectively. C–F display voxel-by-voxel calculation outcomes for the right node presented as mask
overlays on the realigned mean b ⫽ 0 image. G–I display the corresponding histogram distribution plots of the measures from C–E. C and G, Apparent diffusion coefficient obtained from
monoexponential fitting (10⫺3 mm2/s). D and H, Apparent diffusion coefficient (10⫺3 mm2/s), (E and I) apparent kurtosis coefficient (dimensionless), and (F) reduced ␹2 error estimate, all
obtained from the diffusional kurtosis analysis.
els (biexponential and kurtosis) for the analysis of brain DWI
in healthy volunteers and concluded that based on their experimental data, it was not possible to favor 1 of the 2 models.34
As pointed out by Thoeny et al,35 who assessed the influence of the choice of b-values on ADC values obtained from
the parotid gland, it is essential to realize that the choice of
b-values has a great impact on the magnitude of the ADC
values, especially when calculated by using a monoexponential
approach. DWI by using low b-values generally yields higher
ADC values than does DWI by using high b-values. This difference can be attributed to the non-monoexponential behavior of the DWI signal intensity, which points toward the potential benefits of using non-monoexponential fitting (eg,
biexponential or non-Gaussian fitting). If one chooses to perform a non-Gaussian fitting approach on DWI data obtained
over an extended b-value range, it is important to consider the
range of b-values. First, it is essential to include relatively low
b-values (50 s/mm2), as some tissues display rapid signal intensity attenuation with low b-values, followed by a more
gradual signal intensity reduction.1 This initial rapid reduction is thought to be due to vascular capillary perfusion.1 Second, it is advisable to include high b-values (⬎1000 s/mm2), as
this is better for modeling the non-monoexponential nature of
the decaying signal intensities. The anatomy of the head and
neck region, with the intimate presence of air, bone, and acute
changes in tissue bulk, promotes unwanted regional field distortions.36 Therefore, high b-values up to 3000 s/mm2, typically used in brain37 and prostate,30 will not be achievable in
head and neck due to the poorer SNR obtained with the current MR imaging acquisition technique. In the present study,
the diffusion-weighted images acquired with b ⫽ 1500 s/mm2
are the images with the lowest SNR. These images still display
excellent SNR characteristics (median, 42; range, 21– 87). Additionally, the fits of the diffusion-weighted images in the
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745
Fig 3. T2 short tau inversion recovery images for (A) the right neck metastatic lymph node of patient 12 and (B) primary base of tongue tumor of patient 3 (ROIs are indicated with a white
arrow). C and D display the corresponding scatterplots showing the correlation between the Dapp (average apparent diffusion coefficient) and the Kapp (apparent diffusional kurtosis) obtained
from the diffusional kurtosis analysis. The Spearman rank-order correlation coefficient is ⫺0.34 for C, indicating a weak correlation, and ⫺0.90 for D, indicating a strong negative correlation.
present study display beneficial reduced ␹2 goodness-of-fit
characteristics (reduced ␹2 ⬍0.15). Therefore, we are confident about the quality of our data and in particular our choice
of a maximum b-value of 1500 s/mm2.
In the present study the average ADCmono values for various structures in the 17 patients with HNSCC (Table 2) are in
agreement with previously reported values in the literature.18,38,39 We obtained an average ADCmono value of 0.84 ⫾
0.24 ⫻ 10⫺3 mm2/s for metastatic HNSCC neck lymph nodes
(Table 2), which is in close agreement with values previously
reported by Kim et al of 0.77 ⫾ 0.11 ⫻ 10⫺3 mm2/s,38 and
values reported by Vandecaveye et al of 0.85 ⫾ 0.27 ⫻ 10⫺3
mm2/s.39 Furthermore, the obtained ADCmono for the spinal
cord of 0.74 ⫾ 0.08 ⫻ 10⫺3 mm2/s is in the range of the values
described by Wang et al of 1.02 ⫾ 0.15 ⫻ 10⫺3 mm2/s.18
Note, however, that the average diffusion coefficients, the
heterogeneity measures, and the necrosis assessment, as obtained in our study, should be interpreted with caution, due to
the limited number of patients per structure (Table 2). However, our study is unique among DWI studies in HN cancers,
746
Jansen 兩 AJNR 31 兩 Apr 2010 兩 www.ajnr.org
as we performed a non-Gaussian analysis rather than the usual
monoexponential analysis.
The ADC values estimated by using the monoexponential
model were systematically lower than the Dapp values obtained
from the diffusional kurtosis model. This finding is in agreement with those of Lu et al, who observed the same phenomenon in brain tissue.3 Lu et al attributed this discrepancy to the
nature of the diffusional kurtosis model, which is essentially a
second-order polynomial fitting model. This model will always give better fitting results than a linear model.
Our phantom experiments yielded a non-0 Kapp value for
homogenized asparagus (which is known to have structure)
and a 0 Kapp value for water (which does not have structure) as
consistent with literature.8 Previous studies have shown the
advantage of Kapp as a specific measure of tissue structure.3,8
Although Kapp was not necessarily independent of Dapp for all
tumors, 44% of the tumors displayed a low correlation coefficient, suggesting that Kapp might provide additional information related to structure. Interestingly, hDapp was significantly
higher than hADCmono, which indicates that the kurtosis anal-
ysis– derived diffusion coefficient Dapp is likely to be more sensitive to reflect heterogeneity than ADCmono. It was observed
in a subanalysis of the neck nodal metastases that necrosis does
not affect the correlation between Kapp and Dapp, as well as the
heterogeneity measures hADCmono, hDapp, and hKapp. Our assessment of necrosis was limited, as we did not have pathology
as a reference standard. However, we did observe significantly
higher diffusion coefficients for necrotic metastatic lymph
nodes than for non-necrotic nodes, which indicates that our
necrosis assessment was accurate. As reflected in our small
population study, necrosis may not have contributed significantly to the additional information retrieved by using the
kurtosis analysis. Other processes such as hypoxia might play a
role,40 yet our current study design did not incorporate hypoxia measurements. Future studies with pathology might provide better insight. Although the exact nature of Kapp has yet to
be understood, both Dapp and Kapp may be more sensitive to
pathologic changes in tumor physiology than diffusion coefficients obtained from monoexponential analysis.
In future studies, the parameters Dapp and Kapp provided by
the kurtosis model could offer potential value for longitudinal
studies assessing early response in HN cancer patients undergoing chemoradiation therapy.
Recently, there has been a shift to imaging at higher magnetic field strengths (3T or higher), as this provides a higher
SNR, resulting in better spatial resolution.36 However, imaging at higher magnetic field strengths is not without challenges, especially in the head and neck, where images can be
degraded due to susceptibility artifacts.36 Srinivasan et al have
reported initial studies at 3T similar to the ones at 1.5T showing that mean ADC values were able to differentiate between
benign and malignant pathology in the head and neck
regions.16,41
In addition to obtaining multiple diffusion-weighted images over a range of different b-values, one can also acquire
multiple diffusion-weighted images over an increased number
of noncollinear directions (diffusion tensor imaging).42 This
technique yields, in addition to the ADC, the fractional anisotropy, which provides quantitative information about the orientational coherence of cellular structures. Diffusion tensor
imaging has been proved to provide information about architectural structural changes induced by cancer42; however,
head and neck structures do not necessarily reflect a fiber composition. Therefore, the fractional anisotropy measure will
most likely not be affected drastically by neoplastic head and
neck tissue changes.
The study has few limitations. First, it is a cross-sectional
feasibility study that analyzed data from only 17 patients to
assess the benefits of the non-Gaussian kurtosis analysis of
DW images. Second, although we were able to show that the
kurtosis model yields a significantly better fit than does standard monoexponential fitting, we were not able to assess the
pathologic relevance and sensitivity of the calculated diffusion
coefficients. Third, although the MR imaging examinations
were performed at least 1 week after biopsy, there is still a
possibility that these biopsies might have influenced the DWIderived parameters. However, visual inspection did not reveal
any postbiopsy changes. Finally, the exact nature of the nonGaussian behavior still needs to be elucidated.
Conclusions
The use of a diffusional non-Gaussian (kurtosis) model to
analyze DWI data acquired by using an extended b-value
range in HNSCC is feasible. The diffusional non-Gaussian
model yields a significantly better fit of the experimental data
than does monoexponential fitting. Furthermore, it provides
an additional parameter, Kapp, potentially with added value.
Acknowledgments
The authors are grateful to MR imaging technologists Greg
Nyman and colleagues for their great efforts to perform all MR
imaging examinations. We also thank Dr Yousef Mazaheri for
stimulating discussions.
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